Staff Data Scientist

Responsibilities

• Apply statistical inference, causal analysis, and experimentation design to improve LTV/CAC and accelerate feedback loops
• Champion A/B testing by partnering with cross-functional teams to design, analyze, and interpret experiments rigorously, using scalable frameworks and tooling
• Build segmentation frameworks and predictive models (churn, LTV, propensity, etc) to drive targeting, personalization, and lifecycle optimization
• Design and build agentic workflows to automate the data science lifecycle (exploration, modeling, experimentation)
• Use LLMs and AI tools as collaborators to reason about data, generate hypotheses, and iterate on analyses
• Build AI-driven systems for monitoring, diagnosing, and automating business insights and decisions
• Translate data into clear narratives that influence product decisions, marketing campaigns, and executive strategy
• Support automation projects as needed, including anomaly detection, partner data reporting, and internal self-serve tools or dashboards
• Own projects end-to-end - from problem definition through implementation, deployment, and monitoring - while collaborating cross-functionally to drive impact
• Contribute to team excellence through code reviews, technical mentorship, and process improvements

Requirements

• 7-12+ years of experience in data science, analytics, or a related field—ideally at a high-growth startup or fintech company
• Graduate degree in a relevant field (statistics, engineering, science, finance, etc)
• Strong Python and SQL skills, with the ability to transform raw data and build custom datasets when needed
• Highly analytical mindset with a bias toward action and a relentless focus on getting the numbers right
• Ability to clearly communicate complex findings to technical and non-technical audiences
• Comfort owning projects end-to-end and collaborating cross-functionally to drive impact
• Full-stack problem-solving orientation—eager to dive into messy data, test and validate assumptions, and question everything in pursuit of a solution

Nice to Have

• Experience building or scaling experimentation infrastructure
• Experience building or improving ML infra
• Familiarity with dashboarding tools such as Sigma or Looker
• Experience in credit, lending, or card products
• Exposure to lifecycle marketing or prescreen modeling
• Background in time series analysis, forecasting, optimization, or simulation